NYC Taxi Trip Duration Prediction: A Complete Machine Learning Practice from Data Cleaning to Random Forest Modeling

A complete end-to-end machine learning project demonstrating how to process Kaggle competition data, predict NYC taxi trip duration using feature engineering and random forest regression models, and include detailed data visualization workflows.

• Original Author/Maintainer: Ferdous Benyamna, Claudia Garcia Aguiar
• Source Platform: GitHub
• Original Title: nyc_taxi_trip_duration_analysis
• Original Link: https://github.com/fbenyamna-ds/nyc_taxi_trip_duration_analysis
• Publication Date: 2026-05-26

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In urban traffic management, accurately predicting taxi trip duration is crucial for optimizing dispatching, enhancing passenger experience, and reducing operational costs. This project uses NYC taxi data as the research object to build a complete machine learning prediction system. Its core objective is to predict trip duration (trip_duration) based on features such as trip start/end locations, time, and number of passengers.

The project's data comes from the well-known Kaggle competition "NYC Taxi Trip Duration", which is a classic hands-on dataset for data science learners. The project's documentation is written in Spanish, reflecting the diverse contributions of the global open-source community in the field of machine learning education.

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The project uses core data science tools from the Python ecosystem:

• Data Processing: Pandas for structured data manipulation, NumPy for numerical computation support
• Visualization: Matplotlib and Seaborn for generating statistical charts and distribution analysis
• Machine Learning: Scikit-learn provides the Random Forest Regressor model
• Data Acquisition: Kaggle API for automated dataset download

This combination of technologies represents an industry-standard machine learning workflow, suitable for beginners to understand the typical architecture of data science projects.

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The project uses a modular pipeline design, breaking down complex data processing tasks into seven independent stages:

Automatically obtain the competition dataset via the Kaggle API, including the training set (train.csv), test set (test.csv), and submission sample (sample_submission.csv). It is worth noting that using the Kaggle API requires prior account registration and acceptance of competition rules, a design that ensures compliance in data usage.

Raw data often contains issues such as outliers, missing values, and inconsistent formats. The cleaning stage addresses data quality issues, laying the foundation for subsequent analysis.

Understand data distribution characteristics and identify potential patterns and anomalies through statistical summaries and visualization methods; this is an indispensable data understanding step before modeling.